An introduction to the current issues and approaches of question answering (QA) can be found in the web-based reference http://en.wikipedia.org/wiki/Question_answering. Generally, QA is a type of information retrieval. Given a collection of documents (such as the World Wide Web or a local collection) the system should be able to retrieve answers to questions posed in natural language. QA is regarded as requiring more complex natural language processing (NLP) techniques than other types of information retrieval such as document retrieval, and it is sometimes regarded as the next step beyond search engines.
QA research attempts to deal with a wide range of question types including: fact, list, definition, How, Why, hypothetical, semantically-constrained, and cross-lingual questions. Search collections vary from small local document collections, to internal organization documents, to compiled newswire reports, to the World Wide Web.
Closed-domain QA deals with questions under a specific domain, for example medicine or automotive maintenance, and can be seen as an easier task because NLP systems can exploit domain-specific knowledge frequently formalized in ontologies. Open-domain QA deals with questions about nearly everything, and can only rely on general ontologies and world knowledge. On the other hand, these systems usually have much more data available from which to extract the answer.
Alternatively, closed-domain QA might refer to a situation where only a limited type of questions are accepted, such as questions asking for descriptive rather than procedural information.
Access to information is currently dominated by two paradigms. First, a database query that answers questions about what is in a collection of structured records. Second, a search that delivers a collection of document links in response to a query against a collection of unstructured data, for example, text or html.
A major unsolved problem in such information query paradigms is the lack of a computer program capable of accurately answering factual questions based on information included in a collection of documents that can be either structured, unstructured, or both. Such factual questions can be either broad, such as “what are the risks of vitamin K deficiency?”, or narrow, such as “when and where was Hillary Clinton's father born?”
It is a challenge to understand the query, to find appropriate documents that might contain the answer, and to extract the correct answer to be delivered to the user. There is a need to further advance the methodologies for answering open-domain questions.